LEADER 05441nam 22006614a 450 001 9910877843603321 005 20200520144314.0 010 $a1-280-25291-X 010 $a9786610252916 010 $a0-470-34800-3 010 $a0-471-72371-1 010 $a0-471-72372-X 035 $a(CKB)1000000000018988 035 $a(EBL)226562 035 $a(SSID)ssj0000231226 035 $a(PQKBManifestationID)11204243 035 $a(PQKBTitleCode)TC0000231226 035 $a(PQKBWorkID)10206968 035 $a(PQKB)10629975 035 $a(MiAaPQ)EBC226562 035 $a(OCoLC)85820318 035 $a(EXLCZ)991000000000018988 100 $a20021209d2004 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aQuantitative remote sensing of land surfaces /$fShunlin Liang 210 $aHoboken, N.J. $cWiley-Interscience$dc2004 215 $a1 online resource (562 p.) 225 1 $aWiley series in remote sensing 300 $aDescription based upon print version of record. 311 1 $a0-471-28166-2 320 $aIncludes bibliographical references and index. 327 $aQUANTITATIVE REMOTE SENSING OF LAND SURFACES; Contents; Preface; Acronyms; CHAPTER 1 Introduction; 1.1 Quantitative Models in Optical Remote Sensing; 1.2 Basic Concepts; 1.2.1 Digital Numbers; 1.2.2 Radiance; 1.2.3 Solid Angle; 1.2.4 lrradiance; 1.2.5 Bidirectional Reflectances and Albedos; 1.2.6 Extraterrestrial Solar lrradiance; 1.3 Remote Sensing Modeling System; 1.3.1 Scene Generation; 1.3.2 Scene Radiation Modeling; 1.3.3 Atmospheric Radiative Transfer Modeling; 1.3.4 Navigation Modeling; 1.3.5 Sensor Modeling; 1.3.5.1 Spectral Response; 1.3.5.2 Spatial Response 327 $a1.3.6 Mapping and Binning1.4 Summary; References; CHAPTER 2 Atmospheric Shortwave Radiative Transfer Modeling; 2.1 Radiative Transfer Equation .; 2.2 Surface Statistical BRDF Models; 2.2.1 Minnaert Function; 2.2.2 Lommel-Seeliger Function; 2.2.3 Walthall Function; 2.2.4 Staylor-Suttles Function; 2.2.5 Rahman Function; 2.2.6 Kernel Functions; 2.3 Atmospheric Optical Properties; 2.3.1 Rayleigh Scattering; 2.3.2 Mie Scattering; 2.3.3 Aerosol Particle Size Distributions; 2.3.4 Gas Absorption; 2.3.5 Aerosol Climatology; 2.4 Solving Radiative Transfer Equations; 2.4.1 Radiation Field Decomposition 327 $a2.4.2 Numerical Solutions2.4.2.1 Method of Successive Orders of Scattering; 2.4.2.2 Method of Discrete Ordinates; 2.4.3 Approximate Solutions: Two-Stream Algorithms; 2.4.4 Representative Radiative Transfer Solvers (Software Packages); 2.5 Approximate Representation for Incorporating Surface BRDF; 2.6 Summary; References; CHAPTER 3 Canopy Reflectance Modeling; 3.1 Canopy Radiative Transfer Formulation; 3.1.1 Canopy Configuration; 3.1.2 One-Dimensional Radiative Transfer Formulation; 3.1.3 Boundary Conditions; 3.1.4 Hotspot Effects; 3.1.5 Formulations for Heterogeneous Canopies 327 $a3.2 Leaf Optical Models3.2.1 "Plate" Models; 3.2.2 Needleleaf Models; 3.2.3 Ray Tracing Models; 3.2.4 Stochastic Models; 3.2.5 Turbid Medium Models; 3.3 Solving Radiative Transfer Equations; 3.3.1 Approximate Solutions; 3.3.1.1 Models Based on KM Theory; 3.3.1.2 Decomposition of the Canopy Radiation Field; 3.3.1.3 Approximation of Multiple Scattering; 3.3.2 Numerical Solutions: Gauss-Seidel Algorithm; 3.4 Geometric Optical Models; 3.5 Computer Simulation Models; 3.5.1 Monte Carlo Ray Tracing Models; 3.5.1.1 Forward and Reverse Ray Tracing; 3.5.1.2 Canopy Scene Generation 327 $a3.5.1.3 A Forest Ray Tracing Algorithm3.5.1.4 Botanical Plant Modeling System Model; 3.5.1.5 SPRINT Model; 3.5.2 Radiosity Models; 3.5.2.1 Generating the 3D Scene; 3.5.2.2 Calculating the Emission for All Surfaces in the Scene; 3.5.2.3 Computing the View Factors; 3.5.2.4 Solving the Radiosity Equation; 3.5.2.5 Rendering the Scene for a Given Viewpoint and Calculating BRF; 3.5.2.6 Applications; 3.6 Summary; References; CHAPTER 4 Soil and Snow Reflectance Modeling; 4.1 Single Scattering Properties of Snow and Soil; 4.1.1 Optical Properties of Snow; 4.1.2 Optical Properties of Soils 327 $a4.2 Multiple Scattering Solutions for Angular Reflectance from Snow and Soil 330 $aProcessing the vast amounts of data on the Earth's land surface environment generated by NASA's and other international satellite programs is a significant challenge. Filling a gap between the theoretical, physically-based modelling and specific applications, this in-depth study presents practical quantitative algorithms for estimating various land surface variables from remotely sensed observations.A concise review of the basic principles of optical remote sensing as well as practical algorithms for estimating land surface variables quantitatively from remotely sensed observations.Emp 410 0$aWiley series in remote sensing. 606 $aEarth sciences$xRemote sensing 606 $aEnvironmental sciences$xRemote sensing 606 $aRemote sensing 615 0$aEarth sciences$xRemote sensing. 615 0$aEnvironmental sciences$xRemote sensing. 615 0$aRemote sensing. 676 $a550/.28/7 700 $aLiang$b Shunlin$0311491 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910877843603321 996 $aQuantitative remote sensing of land surfaces$9810627 997 $aUNINA